Jeffrey Theodore Heaton

Washington University in St. Louis | WUSTL , Wash U · Sever Institute

This paper introduces the Encog library for Java and C#, a scalable, adaptable, multiplatform machine learning framework that was 1st released in 2008. Encog allows a variety of machine learning models to be applied to datasets using regression, classification, and clustering. Various supported machine learning models can be used interchangeably wi...

Machine learning models, such as neural networks, decision trees, random forests and gradient boosting machines accept a feature vector and provide a prediction. These models learn in a supervised fashion where a set of feature vectors with expected output is provided. It is very common practice to engineer new features from the provided feature se...

Introduction to Neural Networks with Java, Second Edition, introduces the Java programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures are discussed. Training techniques, such as backpropagation, genetic algorithms and simulated an...

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hier...

We present MergeLife, a genetic algorithm (GA) capable of evolving continuous cellular automata (CA) that generate full color dynamic animations according to aesthetic user specifications. A simple 16-byte update rule is introduced that is evolved through an objective function that requires only initial human aesthetic guidelines. This update rule...

Network intrusion detection systems are widely deployed to detect cyberattacks against computer networks. These systems generate large numbers of security alerts that require manual review by security analysts to determine the appropriate courses of action required. The review of these security alerts is time consuming and can cause fatigue for sec...

Feature importance is the process where the individual elements of a machine learning model's feature vector are ranked on their relative importance to the accuracy of that model. Some feature ranking algorithms are specific to a single model type, such as Garson and Goh's neural network weight-based feature ranking algorithm. Other feature ranking...

Feature engineering is a process that augments the feature vector of a machine learning model with calculated values that are designed to enhance the accuracy of a model’s predictions. Research has shown that the accuracy of models such as deep neural networks, support vector machines, and tree/forest-based algorithms sometimes benefit from feature...

Frequent itemset mining is a popular data mining technique. Apriori, Eclat, and FP-Growth are among the most common algorithms for frequent itemset mining. Considerable research has been performed to compare the relative performance between these three algorithms, by evaluating the scalability of each algorithm as the dataset size increases. While...

Machine learning models, such as neural networks, decision trees, random forests and gradient boosting machines accept a feature vector and provide a prediction. These models learn in a supervised fashion where a set of feature vectors with expected output is provided. It is very common practice to engineer new features from the provided feature se...

Like many other areas, predictive modeling is finding great application in the field of Information Assurance (IA). This presentation will present the latest advances in data science and their application to security. The presenter will compare and contrast a variety of current technologies, such as deep learning, general-purpose GPU (GPGPU), long...

Frequent itemset mining is a popular data mining technique. Apriori, Eclat, and FP-Growth are among the most common algorithms for frequent itemset mining. Considerable research has been performed to compare the relative performance between these three algorithms, by evaluating the scalability of each algorithm as the dataset size increases. While...

During the 2009 IEEE Symposium on Industrial Electronics and Applications (ISIEA) a paper was presented that detailed a neural network-based intrusion detection system (IDS) that performed well on the KDD99 dataset. This paper also investigated several hidden layer topologies and attempted to determine the topology that provided the best root mean...

Encog is an advanced Machine Learning Framework for Java, C# and Silverlight. This book focuses on using the neural network capabilities of Encog with the C# programming language. This book begins with an introduction to the kinds of tasks neural networks are suited towards. The reader is shown how to use classification, regression and clustering t...

Encog is an advanced Machine Learning Framework for Java, C# and Silverlight. This book focuses on using the neural network capabilities of Encog with the Java programming language. This book begins with an introduction to the kinds of tasks neural networks are suited towards. The reader is shown how to use classification, regression and clustering...

Introduction to Neural Networks with C#, Second Edition, introduces the C# programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures are discussed. Training techniques, such as backpropagation, genetic algorithms and simulated anneal...